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» Providing k-anonymity in data mining
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KDD
2007
ACM
192views Data Mining» more  KDD 2007»
14 years 7 months ago
Allowing Privacy Protection Algorithms to Jump Out of Local Optimums: An Ordered Greed Framework
Abstract. As more and more person-specific data like health information becomes available, increasing attention is paid to confidentiality and privacy protection. One proposed mode...
Rhonda Chaytor
VLDB
2008
ACM
147views Database» more  VLDB 2008»
14 years 7 months ago
Providing k-anonymity in data mining
In this paper we present extended definitions of k-anonymity and use them to prove that a given data mining model does not violate the k-anonymity of the individuals represented in...
Arik Friedman, Ran Wolff, Assaf Schuster
VLDB
2006
ACM
122views Database» more  VLDB 2006»
14 years 7 months ago
A secure distributed framework for achieving k-anonymity
k-anonymity provides a measure of privacy protection by preventing re-identification of data to fewer than a group of k data items. While algorithms exist for producing k-anonymous...
Wei Jiang, Chris Clifton
DMIN
2009
121views Data Mining» more  DMIN 2009»
13 years 5 months ago
Data Mining in the Real World: Experiences, Challenges, and Recommendations
Abstract - Data mining is used regularly in a variety of industries and is continuing to gain in both popularity and acceptance. However, applying data mining methods to complex re...
Gary M. Weiss
CIKM
2009
Springer
14 years 2 months ago
POkA: identifying pareto-optimal k-anonymous nodes in a domain hierarchy lattice
Data generalization is widely used to protect identities and prevent inference of sensitive information during the public release of microdata. The k-anonymity model has been exte...
Rinku Dewri, Indrajit Ray, Indrakshi Ray, Darrell ...